Browse available Census Bureau datasets with their supported vintage years. Use as the starting point when the right dataset is unknown — ACS5, ACS1, population estimates, decennial census, and the business datasets (County Business Patterns, Economic Census, Nonemployer Statistics) serve different use cases. Pass the dataset_id value to the dataset parameter in other census tools. Each description names the predicates a dataset requires and the geography levels it publishes, both of which vary by dataset.
List the geography levels available for a given Census dataset and year, along with the parent geographies each level requires. Use before querying to confirm that the target geography level exists — ACS1 omits many sub-state levels, and not all datasets support tracts or block groups. The geography_level values returned here are the valid inputs to the geography_level parameter in census_query_data and census_compare_geographies.
Search Census variables by keyword across variable labels and concept groups. Returns variable codes with human-readable labels — use this to go from a concept like "median household income" to the variable code B19013_001E needed for data queries. On ACS datasets it returns both estimate (E suffix) and margin-of-error (M suffix) codes so you can request both; other dataset families publish no margins of error. Also use it to find the predicate codes a dataset filters on, such as NAICS2017 in cbp. When total_matches exceeds the limit, narrow the query to see more specific results.
Fetch full metadata for one or more Census variable codes — label, concept group, predicate type, universe, and margin-of-error sibling references. Use to confirm a variable code before building a query, or to look up what a known code means. On ACS datasets it returns estimate_code and moe_code sibling references so you can request both without a separate search; other dataset families publish no margins of error and carry neither field. It also resolves predicate codes such as NAICS2017 or SEX, confirming a filter dimension exists in a dataset before a query uses it — for the values a dimension accepts rather than the dimension itself, call census_list_predicate_values.
List the codes a Census filter dimension accepts, so a predicates map can be written without guessing. Answers the question left open when census_query_data or census_compare_geographies reports that a dimension was left unset. Which route a dimension takes depends on the vintage: NAICS and POPGROUP always publish a value list in the dataset dictionary, and on the current vintages EMPSZES, LFO, RCPSZES, TAXSTAT, and TYPOP publish none and are enumerated here against the live data endpoint instead. A dictionary value list is a classification shared across Census products rather than a list of what one dataset serves, and roughly half of its codes typically return no rows anywhere — those are checked against the dataset's own published rows and dropped, and the response source field says whether that check ran. The dictionary lists run to thousands of codes and are best narrowed with query. Pass the returned code as the dimension's value in predicates.
Resolve a place name or street address to Census FIPS identifiers. Converts names like "King County, WA", "Seattle, WA", or "Seattle-Tacoma-Bellevue, WA" to the codes required by census_query_data and census_compare_geographies. Use before querying when you have a place name rather than raw FIPS codes — state_fips maps to parent_fips and fips_summary maps to geography_fips in downstream tools, and geography_type is itself the geography_level to query at.
Query a Census dataset for one or more variables at a specific geography. Accepts FIPS codes for the target geography — use census_resolve_geography to convert place names to FIPS when needed. On ACS datasets, labeled estimates and margin-of-error values are returned together. Suppression codes (geography too small, data not collected) are decoded into human-readable reasons rather than passed through as raw negative numbers. Pass geography_fips as "*" to return all geographies at the level within the parent. On the business datasets (cbp, ecnbasic, nonemp), pep/charv, and dec/ddhca, use predicates to filter by industry, size class, or population group — a query that omits one is answered with a default the Census API picks, which is an all-categories total on some dimensions and a single category on others. Each row names the defaults that were applied in applied_filters, and census_list_predicate_values enumerates the codes a dimension accepts. One geography can also come back on more than one row: pep/charv publishes an April estimates base alongside its July estimate, and each row carries a record field saying which it is.
Compare one or more variables across multiple geographies at the same level — all counties in a state, all states nationally, or a named set of specific geographies. Results are sorted and ranked. Covers queries like "rank states by poverty rate", "compare median income across WA counties", or "which census tracts in King County have the highest renter rate." Omit within to compare all geographies nationally at the level. Suppressed values are decoded to human-readable labels rather than passed through as raw negative sentinels. On the business datasets (cbp, ecnbasic, nonemp), pep/charv, and dec/ddhca, use predicates to rank within one industry, size class, or population group — a comparison that omits one ranks on a default the Census API picks, which is an all-categories total on some dimensions and a single category on others. Each row names the defaults that were applied in applied_filters, and census_list_predicate_values enumerates the codes a dimension accepts. A dataset that publishes several records per geography cannot be ranked until one is pinned: pep/charv publishes an April estimates base and a July estimate, so a comparison that pins neither fails with ambiguous_rows rather than giving every geography two ranks — pass predicates {"MONTH": "7"} for the July estimate.